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experiments, including setup of detectors, electronics, and data acquisition systems. Experience analyzing complex experimental and Monte-Carlo simulated data to optimize the analysis of raw detector data in
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some of the following areas: molecular dynamics, Monte Carlo simulations, statistical mechanics, computer programming (e.g., C++, Python), polymer theory, molecular modeling (e.g., of proteins, nucleic
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financial dynamics Apply machine learning and Monte Carlo techniques to simulate complex decision scenarios Contribute to a growing, interdisciplinary field that redefines biodiversity through the lens
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analysis related to sampling, optimisation and learning problems in high dimensions. Examples of current research topics include convergence analysis of Markov processes, efficient Monte Carlo methods, large
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, LROC, ROC, Image perception, mathematical/computational observer models. Both experimental and computational positions available. Skills preferred include any of these: Monte Carlo simulations, benchtop